J. Michael Steele

• V. Pozdnyakov and J. Michael Steele, “Scan Statistics: Pattern Relations and Martingale Methods”. In Handbook of Scan Statistics, edited by (J. Glaz, et al), Springer Verlag, (2019)
• A. Arlotto and J. Michael Steele (2018), A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays, Methodology and Computing in Applied Probability: Special Issue in Memory of Moshe Shaked, 41 (4), pp. 1448-1468.
• J. Michael Steele (2016), The Bruss-Robertson Inequality: Elaborations, Extensions, and Applications, Mathematica Applicanda (Annales Societatis Mathematicae Polonae Series III), 44 (1) (), pp. 3-16.
• A. Arlotto and J. Michael Steele (2016), A Central Limit Theorem for Temporally Non-Homogenous Markov Chains with Applications to Dynamic Programming, Mathematics of Operations Research, 41 (4), pp. 1448-1468.
• Peichao Peng and J. Michael Steele (2016), Sequential Selection of a Monotone Subsequence from a Random Permutation, Proceedings of the American Mathematics Society, 144 (11), pp. 4973-4982.
• A. Arlotto, Elchanan Mossel, J. Michael Steele (2016), Quickest Online Selection of an Increasing Subsequence of Specified Size, Random Structures and Algorithms, 49 (), pp. 235-252.
• A. Arlotto and J. Michael Steele (2016), Beardwood-Halton-Hammersly Theorem for Stationary Ergodic Sequences: a Counter-example, Annals of Applied Probability, 26 (4), pp. 2141-2168.
• V. Posdnyakov and J. Michael Steele (2016), Buses, Bullies, and Bijections, Mathematics Magazine, 89 (3), pp. 167-176. Related
  Links

  MAA Allendoefer Award, 2017
• S. Bhamidi, J. Michael Steele, T. Zaman (2015), Twitter Event Networks and the Superstar Model, Annals of Applied Probability, 25 (5), pp. 2462-2502.
• A. Arlotto, V. Nguyen, J. Michael Steele (2015), Optimal Online Selection of a Monotone Subsequence: A Central Limit Theorem, Stochastic Processes and their Applications, 125 (), pp. 3596-3622.
• All Research from J. Michael Steele »

Past Courses

• MATH5460 - Adv Applied Probability

  The required background is (1) enough math background to understand proof techniques in real analysis (closed sets, uniform covergence, fourier series, etc.) and (2) some exposure to probability theory at an intuitive level (a course at the level of Ross's probability text or some exposure to probability in a statistics class).

• STAT4330 - Stochastic Processes

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

• STAT5330 - Stochastic Processes

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

• Wharton Undergraduate Excellence in Teaching Award, 2010
• Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990
• Fellow, American Statistical Association, 1989
• Fellow, Institute for Mathematical Statistics, 1984

Knowledge @ Wharton

• The Fed Chair Transition and the Future of Central Bank Independence, Knowledge @ Wharton - 01/09/2026
• Special Report: The AI Skills Gap, Knowledge @ Wharton - 01/09/2026
• Six AI Trends Shaping Business, Education, and Markets in 2026, Knowledge @ Wharton - 01/07/2026
• Data-Driven NFL Playoffs and College Football’s Shifting Power, Knowledge @ Wharton - 01/07/2026
• The Psychology of New Year’s Resolutions | Katy Milkman, Knowledge @ Wharton - 01/06/2026
• How AI and Bayesian Models Are Reshaping Sports Analytics, Knowledge @ Wharton - 12/31/2025
• What to Expect for Real Estate in 2026, Knowledge @ Wharton - 12/31/2025
• What’s Ahead for the U.S. Economy?, Knowledge @ Wharton - 12/26/2025
• What Advanced Metrics Reveal About the 2025 NFL Season, Knowledge @ Wharton - 12/24/2025
• How the Job Market Is Shaping Up for 2026, Knowledge @ Wharton - 12/24/2025